Detection of edible oil adulteration based on laser-induced fluorescence spectroscopy and partial least squares-discriminant analysis

This study proposed a method for identifying adulteration in high-quality edible oils by combining laser-induced fluorescence (LIF) technology with partial least squares-discriminant analysis (PLS-DA), aiming to quickly detect low-quality edible oils in the market. Firstly, a laboratory-built LIF system was used to collect fluorescence spectral data of olive oil, sesame oil, peanut oil, and their adulterated samples. Subsequently, PLS-DA was employed to construct adulteration identification models for olive oil, sesame oil, and peanut oil respectively. Finally, the performance of these models was evaluated using a prediction set.The results indicate that the PLS-DA model can accurately capture the differential characteristics in fluorescence spectra between adulterated samples and authentic samples. Under the verification of the experimentally obtained data, a 100% correct classification rate is achieved.This method enables high-precision identification of adulterated edible oil, providing a scientific identification tool for food safety supervision and offers support for technical research.